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GRANZNER, M. STRAUSS, A. REITERER, M. CAO, M. NOVÁK, D.
Original Title
Data-Driven Condition Assessment and Life Cycle Analysis Methods for Dynamically and Fatigue-Loaded Railway Infrastructure Components
Type
journal article in Web of Science
Language
English
Original Abstract
Railway noise barrier constructions are subjected to high aerodynamic loads during the train passages, and the knowledge of their actual structural condition is relevant to assure safety for railway users and to create a basis for forecasting. This paper deals with deterministic and probabilistic approaches for the condition assessment and prediction of the remaining lifetime of railway noise barriers that are embedded in a safety concept that takes into account the damage consequence classes. These approaches are combined into a holistic assessment concept, in other words, a progressive four-stage model in which the information content increases with each model stage and thus successively increases the accuracy of the determined structural conditions at the time of observation and the forecast of the remaining service life of the structure. The analytical methods used in the first stage of the developed holistic framework are based on common static calculations used in engineering practice and, together with expert knowledge and large-scale fatigue test results of noise barrier constructions, form the basis for the subsequent stages. In the second stage of the data-driven condition assessment and life cycle analysis approach, linking routines are implemented that combine the condition assessments from the visual inspections with the additional information from temporary or permanent monitoring systems with the analytical methods. With the application of numerical finite element methods for the development of a digital twin of the noise barrier in the third stage and the probabilistic approaches in the fourth stage, a maximum determination accuracy of the noise barrier condition at the time of observation and prediction accuracy of the remaining service life is achieved. The data-driven condition assessment and life cycle analysis approach enables infrastructure operators to plan their future investments more economically regarding the maintenance, retrofitting, or new construction of railway noise barriers. Ultimately, the aim is to integrate the presented four-stage holistic assessment concept into the specific maintenance and repair planning of infrastructure operators for aerodynamically loaded railway noise barrier constructions.
Keywords
railway noise barrier; fatigue; digital twin; monitoring; condition assessment; lifetime prediction; data-driven
Authors
GRANZNER, M.; STRAUSS, A.; REITERER, M.; CAO, M.; NOVÁK, D.
Released
13. 11. 2023
Publisher
MDPI
Location
BASEL
ISBN
2412-3811
Periodical
Infrastructures
Year of study
8
Number
11
State
Swiss Confederation
Pages from
1
Pages to
19
Pages count
URL
https://www.mdpi.com/2412-3811/8/11/162
Full text in the Digital Library
http://hdl.handle.net/11012/245117
BibTex
@article{BUT187232, author="Maximilian {Granzner} and Alfred {Strauss} and M. {Reiterer} and Maosen {Cao} and Drahomír {Novák}", title="Data-Driven Condition Assessment and Life Cycle Analysis Methods for Dynamically and Fatigue-Loaded Railway Infrastructure Components", journal="Infrastructures", year="2023", volume="8", number="11", pages="1--19", doi="10.3390/infrastructures8110162", issn="2412-3811", url="https://www.mdpi.com/2412-3811/8/11/162" }